Self-Learning Vehicle Detection Dataset for Urban Environments

This dataset was collected as part of a research study aimed at enhancing vehicle detection algorithms through a self-learning approach tailored for urban environments. The primary objective was to minimize dependency on extensive manual labeling and improve adaptability and effectiveness in dynamic urban conditions. The study utilized urban camera infrastructures to gather real-time traffic data, focusing on a diverse range of vehicle types. The dataset includes images captured from traffic cameras situated at the intersection of Calle de Alcalá and Calle de Velázquez in Madrid, Spain, operated by the Madrid City Council. Data collection spanned from November 30, 2023, to December 6, 2023, covering daytime traffic between 8:30 hours and 18:00 hours. A total of 770 images were captured at approximately 5-minute intervals. This dataset specifically targets five vehicle types: buses, cars, motorcycles, trucks, and vans, chosen to encompass a wide range of vehicle sizes, shapes, and functionalities commonly encountered in city traffic. A subset of 134 images was manually labeled, into sets for training, validation (fine-tuning phase), and validation (self-training phase). The remaining 653 images were labeled automatically via the self-learning process proposed in the research.

本数据集为一项适配城市环境的自学习车辆检测算法优化研究的采集成果。其核心目标为减少对大规模人工标注的依赖，并提升算法在动态城市交通场景中的适应性与检测效能。本研究依托城市监控摄像头基础设施采集实时交通数据，重点覆盖多样化的车辆类型。数据集包含由西班牙马德里市议会运营的、设于马德里阿尔卡拉大道（Calle de Alcalá）与贝拉斯克斯大道（Calle de Velázquez）交叉口的交通摄像头所拍摄的图像。数据采集时段为2023年11月30日至2023年12月6日，采集范围为每日8:30至18:00的日间交通时段。本次采集以约5分钟为间隔，共获取770张图像。本数据集专门涵盖五类车辆：公交车、轿车、摩托车、货车与厢式车，覆盖城市交通中常见的各类尺寸、外形及功能的车辆。研究人员对其中134张图像进行了人工标注，并将其划分为训练集、微调阶段验证集以及自训练阶段验证集三个子集。剩余653张图像则通过本研究提出的自学习流程完成自动标注。